Spring Boot(五十六)：基于Redis的搜索栏热搜功能

1 功能要求

使用SpringBoot和redis实现一个简单的热搜功能具备以下功能

1. 搜索栏展示当前登陆的个人用户的搜索历史记录删除个人历史记录
2. 用户在搜索栏输入某字符则将该字符记录下来 以zset格式存储的redis中记录该字符被搜索的个数以及当前的时间戳 用了DFA算法感兴趣的自己百度学习吧
3. 每当用户查询了已在redis存在了的字符时则直接累加个数 用来获取平台上最热查询的十条数据。可以自己写接口或者直接在redis中添加一些预备好的关键词
4. 最后还要做不雅文字过滤功能

代码实现热搜与个人搜索记录功能主要controller层下几个方法就行了

1. 向redis 添加热搜词汇(添加的时候使用下面不雅文字过滤的方法来过滤下这个词汇合法再去存储
2. 每次点击给相关词热度 +1
3. 根据key搜索相关最热的前十名
4. 插入个人搜索记录
5. 查询个人搜索记录

2 代码实现

2.1 项目结构如下

2.2 引入相关maven依赖

    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.springframework.boot/spring-boot-starter-data-redis -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-redis</artifactId>
            <version>2.7.0</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.commons/commons-lang3 -->
        <dependency>
            <groupId>org.apache.commons</groupId>
            <artifactId>commons-lang3</artifactId>
            <version>3.12.0</version>
        </dependency>

    </dependencies>

2.3 application.yml配置

spring:
  redis:
    #数据库索引
    database: 0
    host: 192.168.222.131
    port: 6379
    password: 123456
    lettuce:
      pool:
        #最大连接数
        max-active: 8
        #最大阻塞等待时间(负数表示没限制)
        max-wait: -1
        #最大空闲
        max-idle: 8
        #最小空闲
        min-idle: 0
        #连接超时时间
    timeout: 10000

2.4 创建RedisKeyUtils工具类

package com.example.demo.Utils;

public class RedisKeyUtils {

    /**
     * 分隔符号
     */
    private static final String SPLIT = ":";

    private static final String SEARCH = "search";

    private static final String SEARCH_HISTORY = "search-history";

    private static final String HOT_SEARCH = "hot-search";

    private static final String SEARCH_TIME = "search-time";

    /**
     * 每个用户的个人搜索记录hash
     */
    public static String getSearchHistoryKey(String userId){
        return SEARCH + SPLIT + SEARCH_HISTORY + SPLIT + userId;
    }

    /**
     * 总的热搜zset
     */
    public static String getHotSearchKey(){
        return SEARCH + SPLIT + HOT_SEARCH;
    }


    /**
     * 每个搜索记录的时间戳记录key-value
     */
    public static String getSearchTimeKey(String searchKey){
        return SEARCH + SPLIT + SEARCH_TIME + SPLIT + searchKey;
    }

}

2.5 核心搜索文件

redis搜索接口

package com.example.demo.Service;

import java.util.List;

public interface RedisService {

    //新增一条该userid用户在搜索栏的历史记录
    //searchkey 代表输入的关键词
    int addSearchHistoryByUserId(String userid, String searchkey);

    //删除个人历史数据
    Long delSearchHistoryByUserId(String userid, String searchkey);

    //获取个人历史数据列表
    List<String> getSearchHistoryByUserId(String userid);

    //新增一条热词搜索记录将用户输入的热词存储下来
    int incrementScoreByUserId(String searchkey);

    //根据searchkey搜索其相关最热的前十名 (如果searchkey为null空则返回redis存储的前十最热词条)
    List<String> getHotList(String searchkey);

    //每次点击给相关词searchkey热度 +1
    int incrementScore(String searchkey);
}

接口实现类

package com.example.demo.Service.Impl;

import com.example.demo.Service.RedisService;
import com.example.demo.Utils.RedisKeyUtils;
import org.apache.commons.lang3.StringUtils;
import org.springframework.data.redis.core.*;
import org.springframework.stereotype.Service;
import org.springframework.transaction.annotation.Transactional;

import javax.annotation.Resource;
import java.util.*;
import java.util.concurrent.TimeUnit;

@Transactional
@Service("redisService")
public class RedisServiceImpl implements RedisService {

    //导入数据源
//    @Resource(name = "redisSearchTemplate")
    @Resource
    private StringRedisTemplate redisSearchTemplate;


    //新增一条该userid用户在搜索栏的历史记录
    //searchkey 代表输入的关键词
    @Override
    public int addSearchHistoryByUserId(String userid, String searchkey) {
        String shistory = RedisKeyUtils.getSearchHistoryKey(userid);
        boolean b = redisSearchTemplate.hasKey(shistory);
        if (b) {
            Object hk = redisSearchTemplate.opsForHash().get(shistory, searchkey);
            if (hk != null) {
                return 1;
            }else{
                redisSearchTemplate.opsForHash().put(shistory, searchkey, "1");
            }
        }else{
            redisSearchTemplate.opsForHash().put(shistory, searchkey, "1");
        }
        return 1;
    }

    //删除个人历史数据
    @Override
    public Long delSearchHistoryByUserId(String userid, String searchkey) {
        String shistory = RedisKeyUtils.getSearchHistoryKey(userid);
        return redisSearchTemplate.opsForHash().delete(shistory, searchkey);
    }

    //获取个人历史数据列表
    @Override
    public List<String> getSearchHistoryByUserId(String userid) {
        List<String> stringList = null;
        String shistory = RedisKeyUtils.getSearchHistoryKey(userid);
        boolean b = redisSearchTemplate.hasKey(shistory);
        if(b){
            Cursor<Map.Entry<Object, Object>> cursor = redisSearchTemplate.opsForHash().scan(shistory, ScanOptions.NONE);
            while (cursor.hasNext()) {
                Map.Entry<Object, Object> map = cursor.next();
                String key = map.getKey().toString();
                stringList.add(key);
            }
            return stringList;
        }
        return null;
    }

    //新增一条热词搜索记录将用户输入的热词存储下来
    @Override
    public int incrementScoreByUserId(String searchkey) {
        Long now = System.currentTimeMillis();
        ZSetOperations zSetOperations = redisSearchTemplate.opsForZSet();
        ValueOperations<String, String> valueOperations = redisSearchTemplate.opsForValue();
        List<String> title = new ArrayList<>();
        title.add(searchkey);
        for (int i = 0, lengh = title.size(); i < lengh; i++) {
            String tle = title.get(i);
            try {
                if (zSetOperations.score("title", tle) <= 0) {
                    zSetOperations.add("title", tle, 0);
                    valueOperations.set(tle, String.valueOf(now));
                }
            } catch (Exception e) {
                zSetOperations.add("title", tle, 0);
                valueOperations.set(tle, String.valueOf(now));
            }
        }
        return 1;
    }

    //根据searchkey搜索其相关最热的前十名 (如果searchkey为null空则返回redis存储的前十最热词条)
    @Override
    public List<String> getHotList(String searchkey) {
        String key = searchkey;
        Long now = System.currentTimeMillis();
        List<String> result = new ArrayList<>();
        ZSetOperations zSetOperations = redisSearchTemplate.opsForZSet();
        ValueOperations<String, String> valueOperations = redisSearchTemplate.opsForValue();
        Set<String> value = zSetOperations.reverseRangeByScore("title", 0, Double.MAX_VALUE);
        //key不为空的时候 推荐相关的最热前十名
        if(StringUtils.isNotEmpty(searchkey)){
            for (String val : value) {
                if (StringUtils.containsIgnoreCase(val, key)) {
                    if (result.size() > 9) {//只返回最热的前十名
                        break;
                    }
                    Long time = Long.valueOf(valueOperations.get(val));
                    if ((now - time) < 2592000000L) {//返回最近一个月的数据
                        result.add(val);
                    } else {//时间超过一个月没搜索就把这个词热度归0
                        zSetOperations.add("title", val, 0);
                    }
                }
            }
        }else{
            for (String val : value) {
                if (result.size() > 9) {//只返回最热的前十名
                    break;
                }
                Long time = Long.valueOf(valueOperations.get(val));
                if ((now - time) < 2592000000L) {//返回最近一个月的数据
                    result.add(val);
                } else {//时间超过一个月没搜索就把这个词热度归0
                    zSetOperations.add("title", val, 0);
                }
            }
        }
        return result;
    }

    //每次点击给相关词searchkey热度 +1
    @Override
    public int incrementScore(String searchkey) {
        String key = searchkey;
        Long now = System.currentTimeMillis();
        ZSetOperations zSetOperations = redisSearchTemplate.opsForZSet();
        ValueOperations<String, String> valueOperations = redisSearchTemplate.opsForValue();
        zSetOperations.incrementScore("title", key, 1);
        valueOperations.getAndSet(key, String.valueOf(now));
        return 1;
    }
}

2.6 不雅字过滤

实现原理

简单原理如下图所示使用了DFA算法创建结点类里面包含是否是敏感词结束符以及一个HashMap哈希里key值存储的是敏感词的一个词value指向下一个结点即指向下一个词一个哈希表中可以存放多个值比如赌博、赌黄这两个都是敏感词。

实现方法

敏感词库的初始化这里主要工作是读取敏感词文件在内存中构建好敏感词的Map节点。

package com.example.demo.Config;

import org.springframework.context.annotation.Configuration;
import org.springframework.core.io.ClassPathResource;
import java.io.*;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;


//屏蔽敏感词初始化
@Configuration
@SuppressWarnings({ "rawtypes", "unchecked" })
public class SensitiveWordInit {
    // 字符编码
    private String ENCODING = "UTF-8";
    // 初始化敏感字库
    public Map initKeyWord() throws IOException {
        // 读取敏感词库 ,存入Set中
        Set<String> wordSet = readSensitiveWordFile();
        // 将敏感词库加入到HashMap中//确定有穷自动机DFA
        return addSensitiveWordToHashMap(wordSet);
    }

    // 读取敏感词库 ,存入HashMap中
    private Set<String> readSensitiveWordFile() throws IOException {
        Set<String> wordSet = null;
        ClassPathResource classPathResource = new ClassPathResource("static/word1.txt");
        InputStream inputStream = classPathResource.getInputStream();
        //敏感词库
        try {
            // 读取文件输入流
            InputStreamReader read = new InputStreamReader(inputStream, ENCODING);
            // 文件是否是文件 和 是否存在
            wordSet = new HashSet<String>();
            // StringBuffer sb = new StringBuffer();
            // BufferedReader是包装类先把字符读到缓存里到缓存满了再读入内存提高了读的效率。
            BufferedReader br = new BufferedReader(read);
            String txt = null;
            // 读取文件将文件内容放入到set中
            while ((txt = br.readLine()) != null) {
                wordSet.add(txt);
            }
            br.close();
            // 关闭文件流
            read.close();
        } catch (Exception e) {
            e.printStackTrace();
        }
        return wordSet;
    }
    // 将HashSet中的敏感词,存入HashMap中
    private Map addSensitiveWordToHashMap(Set<String> wordSet) {
        // 初始化敏感词容器减少扩容操作
        Map wordMap = new HashMap(wordSet.size());
        for (String word : wordSet) {
            Map nowMap = wordMap;
            for (int i = 0; i < word.length(); i++) {
                // 转换成char型
                char keyChar = word.charAt(i);
                // 获取
                Object tempMap = nowMap.get(keyChar);
                // 如果存在该key直接赋值
                if (tempMap != null) {
                    nowMap = (Map) tempMap;
                }
                // 不存在则则构建一个map同时将isEnd设置为0因为他不是最后一个
                else {
                    // 设置标志位
                    Map<String, String> newMap = new HashMap<String, String>();
                    newMap.put("isEnd", "0");
                    // 添加到集合
                    nowMap.put(keyChar, newMap);
                    nowMap = newMap;
                }
                // 最后一个
                if (i == word.length() - 1) {
                    nowMap.put("isEnd", "1");
                }
            }
        }
        return wordMap;
    }
}

最后刚才的SensitiveWordInit.java里面用到了word.txt 文件放到你项目里面的 resources 目录下的 static 目录中这个文件就是不雅文字大全也需要您与时俱进的更新项目启动的时候会加载该文件。

敏感词过滤器

敏感词过滤器主要功能是初始化敏感词库敏感词的过滤以及替换

package com.example.demo.Filter;

import com.example.demo.Config.SensitiveWordInit;
import org.springframework.stereotype.Component;

import java.io.IOException;
import java.util.HashSet;
import java.util.Iterator;
import java.util.Map;
import java.util.Set;

@Component
public class SensitiveFilter {
    /**
     * 敏感词过滤器利用DFA算法  进行敏感词过滤
     */
    private Map sensitiveWordMap = null;

    /**
     * 最小匹配规则如敏感词库["中国","中国人"]语句"我是中国人"匹配结果我是[中国]人
     */
    public static int minMatchType = 1;

    /**
     * 最大匹配规则如敏感词库["中国","中国人"]语句"我是中国人"匹配结果我是[中国人]
     */
    public static int maxMatchType = 2;

    /**
     * 敏感词替换词
     */
    public static String placeHolder = "**";

    // 单例
    private static SensitiveFilter instance = null;

    /**
     * 构造函数初始化敏感词库
     */
    private SensitiveFilter() throws IOException {
        sensitiveWordMap = new SensitiveWordInit().initKeyWord();
    }

    /**
     * 获取单例
     */
    public static SensitiveFilter getInstance() throws IOException {
        if (null == instance) {
            instance = new SensitiveFilter();
        }
        return instance;
    }

    /**
     * 获取文字中的敏感词
     */
    public Set<String> getSensitiveWord(String txt, int matchType) {
        Set<String> sensitiveWordList = new HashSet<>();
        for (int i = 0; i < txt.length(); i++) {
            // 判断是否包含敏感字符
            int length = CheckSensitiveWord(txt, i, matchType);
            // 存在,加入list中
            if (length > 0) {
                sensitiveWordList.add(txt.substring(i, i + length));
                // 减1的原因是因为for会自增
                i = i + length - 1;
            }
        }
        return sensitiveWordList;
    }


    /**
     * 替换敏感字字符,使用了默认的替换符合默认最小匹配规则
     */
    public String replaceSensitiveWord(String txt) {
        return replaceSensitiveWord(txt, minMatchType ,placeHolder);
    }

    /**
     * 替换敏感字字符,使用了默认的替换符合
     */
    public String replaceSensitiveWord(String txt, int matchType) {
        return replaceSensitiveWord(txt, matchType,placeHolder);
    }

    /**
     * 替换敏感字字符
     */
    public String replaceSensitiveWord(String txt, int matchType,
                                       String replaceChar) {
        String resultTxt = txt;
        // 获取所有的敏感词
        Set<String> set = getSensitiveWord(txt, matchType);
        Iterator<String> iterator = set.iterator();
        String word = null;
        String replaceString = null;
        while (iterator.hasNext()) {
            word = iterator.next();
            replaceString = getReplaceChars(replaceChar, word.length());
            resultTxt = resultTxt.replaceAll(word, replaceString);
        }
        return resultTxt;
    }

    /**
     * 获取替换字符串
     */
    private String getReplaceChars(String replaceChar, int length) {
        StringBuilder resultReplace = new StringBuilder(replaceChar);
        for (int i = 1; i < length; i++) {
            resultReplace.append(replaceChar);
        }
        return resultReplace.toString();
    }

    /**
     * 检查文字中是否包含敏感字符检查规则如下<br>
     * 如果存在则返回敏感词字符的长度不存在返回0
     * 核心
     */
    public int CheckSensitiveWord(String txt, int beginIndex, int matchType) {
        // 敏感词结束标识位用于敏感词只有1的情况结束
        boolean flag = false;
        // 匹配标识数默认为0
        int matchFlag = 0;
        Map nowMap = sensitiveWordMap;
        for (int i = beginIndex; i < txt.length(); i++) {
            char word = txt.charAt(i);
            // 获取指定key
            nowMap = (Map) nowMap.get(word);
            // 存在则判断是否为最后一个
            if (nowMap != null) {
                // 找到相应key匹配标识+1
                matchFlag++;
                // 如果为最后一个匹配规则,结束循环返回匹配标识数
                if ("1".equals(nowMap.get("isEnd"))) {
                    // 结束标志位为true
                    flag = true;
                    // 最小规则直接返回,最大规则还需继续查找
                    if (SensitiveFilter.minMatchType == matchType) {
                        break;
                    }
                }
            }
            // 不存在直接返回
            else {
                break;
            }
        }

        // 匹配长度如果匹配上了最小匹配长度或者最大匹配长度
        if (SensitiveFilter.maxMatchType == matchType || SensitiveFilter.minMatchType == matchType){
            //长度必须大于等于1为词或者敏感词库还没有结束(匹配了一半)flag为false
            if(matchFlag < 2 || !flag){
                matchFlag = 0;
            }
        }
        return matchFlag;
    }
}

测试不雅文字过滤器

package com.example.demo.Controller;

import com.example.demo.Filter.SensitiveFilter;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;



@RestController
public class SensitiveController {

    private static Logger logger = LoggerFactory.getLogger(SensitiveController.class);

    @Autowired
    SensitiveFilter sensitiveFilter;


    @GetMapping("/sensitive")
    public String sensitive(String keyword){
        String s = sensitiveFilter.replaceSensitiveWord(keyword);
        return s;
    }
}

直接测试

测试热点搜索接口

package com.example.demo.Controller;

import com.example.demo.Service.RedisService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;

import java.util.List;

@RestController
public class SearchHistoryController {

    @Autowired
    RedisService redisService;


    @GetMapping("/add")
    public String addSearchHistoryByUserId(String userId, String searchKey) {
        redisService.addSearchHistoryByUserId(userId, searchKey);
        redisService.incrementScore(searchKey);
        return null;
    }

    /**
     * 删除个人历史数据
     */
    @GetMapping("/del")
    public Long delSearchHistoryByUserId(String userId, String searchKey) {
        return redisService.delSearchHistoryByUserId(userId, searchKey);
    }

    /**
     * 获取个人历史数据列表
     */
    @GetMapping("/getUser")
    public List<String> getSearchHistoryByUserId(String userId) {
        return redisService.getSearchHistoryByUserId(userId);
    }



    /**
     * 根据searchKey搜索其相关最热的前十名 (如果searchKey为null空则返回redis存储的前十最热词条)
     */
    @GetMapping("/getHot")
    public List<String> getHotList(String searchKey) {
        return redisService.getHotList(searchKey);
    }

}

测试搜索接口

查询搜索热点

代码如下https://download.csdn.net/download/u013938578/87390464?spm=1001.2014.3001.5503